Overview

Dataset statistics

Number of variables23
Number of observations11905
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory2.1 MiB
Average record size in memory184.0 B

Variable types

Numeric22
Categorical1

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
df_index is highly correlated with defectsHigh correlation
loc is highly correlated with v(g) and 17 other fieldsHigh correlation
v(g) is highly correlated with loc and 15 other fieldsHigh correlation
ev(g) is highly correlated with v(g) and 3 other fieldsHigh correlation
iv(g) is highly correlated with loc and 15 other fieldsHigh correlation
n is highly correlated with loc and 16 other fieldsHigh correlation
v is highly correlated with loc and 16 other fieldsHigh correlation
l is highly correlated with loc and 4 other fieldsHigh correlation
d is highly correlated with loc and 16 other fieldsHigh correlation
i is highly correlated with loc and 13 other fieldsHigh correlation
e is highly correlated with loc and 16 other fieldsHigh correlation
b is highly correlated with loc and 16 other fieldsHigh correlation
t is highly correlated with loc and 16 other fieldsHigh correlation
lOCode is highly correlated with loc and 16 other fieldsHigh correlation
lOComment is highly correlated with loc and 13 other fieldsHigh correlation
lOBlank is highly correlated with loc and 13 other fieldsHigh correlation
uniq_Op is highly correlated with loc and 16 other fieldsHigh correlation
uniq_Opnd is highly correlated with loc and 16 other fieldsHigh correlation
total_Op is highly correlated with loc and 16 other fieldsHigh correlation
total_Opnd is highly correlated with loc and 16 other fieldsHigh correlation
branchCount is highly correlated with loc and 15 other fieldsHigh correlation
defects is highly correlated with df_indexHigh correlation
df_index is highly correlated with defectsHigh correlation
loc is highly correlated with v(g) and 17 other fieldsHigh correlation
v(g) is highly correlated with loc and 15 other fieldsHigh correlation
ev(g) is highly correlated with loc and 4 other fieldsHigh correlation
iv(g) is highly correlated with loc and 15 other fieldsHigh correlation
n is highly correlated with loc and 16 other fieldsHigh correlation
v is highly correlated with loc and 16 other fieldsHigh correlation
d is highly correlated with loc and 14 other fieldsHigh correlation
i is highly correlated with loc and 8 other fieldsHigh correlation
e is highly correlated with loc and 13 other fieldsHigh correlation
b is highly correlated with loc and 16 other fieldsHigh correlation
t is highly correlated with loc and 13 other fieldsHigh correlation
lOCode is highly correlated with loc and 15 other fieldsHigh correlation
lOComment is highly correlated with loc and 7 other fieldsHigh correlation
lOBlank is highly correlated with loc and 15 other fieldsHigh correlation
uniq_Op is highly correlated with loc and 11 other fieldsHigh correlation
uniq_Opnd is highly correlated with loc and 16 other fieldsHigh correlation
total_Op is highly correlated with loc and 17 other fieldsHigh correlation
total_Opnd is highly correlated with loc and 16 other fieldsHigh correlation
branchCount is highly correlated with loc and 15 other fieldsHigh correlation
defects is highly correlated with df_indexHigh correlation
loc is highly correlated with v(g) and 14 other fieldsHigh correlation
v(g) is highly correlated with loc and 5 other fieldsHigh correlation
ev(g) is highly correlated with v(g) and 1 other fieldsHigh correlation
iv(g) is highly correlated with loc and 2 other fieldsHigh correlation
n is highly correlated with loc and 12 other fieldsHigh correlation
v is highly correlated with loc and 12 other fieldsHigh correlation
l is highly correlated with loc and 3 other fieldsHigh correlation
d is highly correlated with loc and 13 other fieldsHigh correlation
i is highly correlated with n and 11 other fieldsHigh correlation
e is highly correlated with loc and 12 other fieldsHigh correlation
b is highly correlated with loc and 12 other fieldsHigh correlation
t is highly correlated with loc and 12 other fieldsHigh correlation
lOCode is highly correlated with loc and 12 other fieldsHigh correlation
lOComment is highly correlated with lOBlankHigh correlation
lOBlank is highly correlated with loc and 13 other fieldsHigh correlation
uniq_Op is highly correlated with v(g) and 12 other fieldsHigh correlation
uniq_Opnd is highly correlated with loc and 12 other fieldsHigh correlation
total_Op is highly correlated with loc and 12 other fieldsHigh correlation
total_Opnd is highly correlated with loc and 12 other fieldsHigh correlation
branchCount is highly correlated with loc and 4 other fieldsHigh correlation
df_index is highly correlated with defectsHigh correlation
loc is highly correlated with v(g) and 16 other fieldsHigh correlation
v(g) is highly correlated with loc and 15 other fieldsHigh correlation
ev(g) is highly correlated with loc and 15 other fieldsHigh correlation
iv(g) is highly correlated with loc and 15 other fieldsHigh correlation
n is highly correlated with loc and 16 other fieldsHigh correlation
v is highly correlated with loc and 17 other fieldsHigh correlation
d is highly correlated with loc and 15 other fieldsHigh correlation
i is highly correlated with v and 3 other fieldsHigh correlation
e is highly correlated with loc and 16 other fieldsHigh correlation
b is highly correlated with loc and 17 other fieldsHigh correlation
t is highly correlated with loc and 16 other fieldsHigh correlation
lOCode is highly correlated with loc and 16 other fieldsHigh correlation
lOComment is highly correlated with loc and 11 other fieldsHigh correlation
lOBlank is highly correlated with loc and 16 other fieldsHigh correlation
uniq_Op is highly correlated with loc and 16 other fieldsHigh correlation
uniq_Opnd is highly correlated with loc and 17 other fieldsHigh correlation
total_Op is highly correlated with loc and 16 other fieldsHigh correlation
total_Opnd is highly correlated with loc and 17 other fieldsHigh correlation
branchCount is highly correlated with loc and 15 other fieldsHigh correlation
defects is highly correlated with df_indexHigh correlation
iv(g) is highly skewed (γ1 = 21.18802343) Skewed
e is highly skewed (γ1 = 47.81012766) Skewed
t is highly skewed (γ1 = 47.72062725) Skewed
locCodeAndComment is highly skewed (γ1 = 22.85647256) Skewed
n has 1333 (11.2%) zeros Zeros
v has 1374 (11.5%) zeros Zeros
l has 1405 (11.8%) zeros Zeros
d has 1395 (11.7%) zeros Zeros
i has 1395 (11.7%) zeros Zeros
e has 1395 (11.7%) zeros Zeros
b has 1897 (15.9%) zeros Zeros
t has 1395 (11.7%) zeros Zeros
lOCode has 1575 (13.2%) zeros Zeros
lOComment has 7771 (65.3%) zeros Zeros
lOBlank has 3568 (30.0%) zeros Zeros
locCodeAndComment has 10524 (88.4%) zeros Zeros
uniq_Op has 1332 (11.2%) zeros Zeros
uniq_Opnd has 1394 (11.7%) zeros Zeros
total_Op has 1332 (11.2%) zeros Zeros
total_Opnd has 1394 (11.7%) zeros Zeros

Reproduction

Analysis started2022-06-16 10:17:18.752956
Analysis finished2022-06-16 10:18:17.365152
Duration58.61 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10885
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4997.555985
Minimum0
Maximum10884
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:17.482827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile198
Q11956
median4932
Q37908
95-th percentile10288.8
Maximum10884
Range10884
Interquartile range (IQR)5952

Descriptive statistics

Standard deviation3337.425302
Coefficient of variation (CV)0.6678114886
Kurtosis-1.268482836
Mean4997.555985
Median Absolute Deviation (MAD)2976
Skewness0.07935958626
Sum59495904
Variance11138407.64
MonotonicityNot monotonic
2022-06-16T15:48:17.612560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
< 0.1%
4433
 
< 0.1%
4553
 
< 0.1%
4593
 
< 0.1%
4473
 
< 0.1%
4773
 
< 0.1%
4393
 
< 0.1%
4513
 
< 0.1%
4693
 
< 0.1%
4313
 
< 0.1%
Other values (10875)11875
99.7%
ValueCountFrequency (%)
03
< 0.1%
13
< 0.1%
23
< 0.1%
33
< 0.1%
43
< 0.1%
53
< 0.1%
63
< 0.1%
73
< 0.1%
83
< 0.1%
93
< 0.1%
ValueCountFrequency (%)
108841
< 0.1%
108831
< 0.1%
108821
< 0.1%
108811
< 0.1%
108801
< 0.1%
108791
< 0.1%
108781
< 0.1%
108771
< 0.1%
108761
< 0.1%
108751
< 0.1%

loc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct369
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.27411172
Minimum1
Maximum3442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:17.740314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median22
Q345
95-th percentile137
Maximum3442
Range3441
Interquartile range (IQR)34

Descriptive statistics

Standard deviation75.58487701
Coefficient of variation (CV)1.831290217
Kurtosis453.4626611
Mean41.27411172
Median Absolute Deviation (MAD)14
Skewness14.60639304
Sum491368.3
Variance5713.073632
MonotonicityNot monotonic
2022-06-16T15:48:17.860505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4997
 
8.4%
5397
 
3.3%
7364
 
3.1%
11318
 
2.7%
8300
 
2.5%
12290
 
2.4%
14289
 
2.4%
17284
 
2.4%
15277
 
2.3%
10273
 
2.3%
Other values (359)8116
68.2%
ValueCountFrequency (%)
110
 
0.1%
1.13
 
< 0.1%
219
 
0.2%
342
 
0.4%
4997
8.4%
5397
 
3.3%
6271
 
2.3%
7364
 
3.1%
8300
 
2.5%
9259
 
2.2%
ValueCountFrequency (%)
34421
< 0.1%
18821
< 0.1%
18241
< 0.1%
15321
< 0.1%
14111
< 0.1%
12801
< 0.1%
12751
< 0.1%
12431
< 0.1%
11291
< 0.1%
9021
< 0.1%

v(g)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.244367913
Minimum1
Maximum470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:17.986691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile20
Maximum470
Range469
Interquartile range (IQR)6

Descriptive statistics

Standard deviation12.7785482
Coefficient of variation (CV)2.046411803
Kurtosis390.3758592
Mean6.244367913
Median Absolute Deviation (MAD)2
Skewness15.06501551
Sum74339.2
Variance163.2912941
MonotonicityNot monotonic
2022-06-16T15:48:18.106784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13013
25.3%
21906
16.0%
31439
12.1%
41053
 
8.8%
5837
 
7.0%
6639
 
5.4%
7443
 
3.7%
8339
 
2.8%
9278
 
2.3%
10264
 
2.2%
Other values (102)1694
14.2%
ValueCountFrequency (%)
13013
25.3%
1.43
 
< 0.1%
21906
16.0%
31439
12.1%
41053
 
8.8%
5837
 
7.0%
6639
 
5.4%
7443
 
3.7%
8339
 
2.8%
9278
 
2.3%
ValueCountFrequency (%)
4701
< 0.1%
4041
< 0.1%
4021
< 0.1%
2861
< 0.1%
2731
< 0.1%
2681
< 0.1%
2631
< 0.1%
2071
< 0.1%
2011
< 0.1%
1801
< 0.1%

ev(g)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct75
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.321142377
Minimum1
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:18.227204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile12
Maximum165
Range164
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.670843923
Coefficient of variation (CV)2.0085992
Kurtosis120.7946854
Mean3.321142377
Median Absolute Deviation (MAD)0
Skewness8.446779358
Sum39538.2
Variance44.50015864
MonotonicityNot monotonic
2022-06-16T15:48:18.345445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18257
69.4%
3804
 
6.8%
4575
 
4.8%
5476
 
4.0%
6331
 
2.8%
7234
 
2.0%
8183
 
1.5%
9159
 
1.3%
10126
 
1.1%
1195
 
0.8%
Other values (65)665
 
5.6%
ValueCountFrequency (%)
18257
69.4%
1.43
 
< 0.1%
3804
 
6.8%
4575
 
4.8%
5476
 
4.0%
6331
 
2.8%
7234
 
2.0%
8183
 
1.5%
9159
 
1.3%
10126
 
1.1%
ValueCountFrequency (%)
1651
< 0.1%
1401
< 0.1%
1331
< 0.1%
1251
< 0.1%
1221
< 0.1%
1171
< 0.1%
1131
< 0.1%
1121
< 0.1%
1111
< 0.1%
951
< 0.1%

iv(g)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct84
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.966417472
Minimum1
Maximum402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:18.474349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile12
Maximum402
Range401
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.949609032
Coefficient of variation (CV)2.256345706
Kurtosis739.8495736
Mean3.966417472
Median Absolute Deviation (MAD)1
Skewness21.18802343
Sum47220.2
Variance80.09550182
MonotonicityNot monotonic
2022-06-16T15:48:18.599369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14289
36.0%
22639
22.2%
31454
 
12.2%
4909
 
7.6%
5605
 
5.1%
6408
 
3.4%
7350
 
2.9%
8197
 
1.7%
9164
 
1.4%
10123
 
1.0%
Other values (74)767
 
6.4%
ValueCountFrequency (%)
14289
36.0%
1.43
 
< 0.1%
22639
22.2%
31454
 
12.2%
4909
 
7.6%
5605
 
5.1%
6408
 
3.4%
7350
 
2.9%
8197
 
1.7%
9164
 
1.4%
ValueCountFrequency (%)
4021
< 0.1%
3851
< 0.1%
2561
< 0.1%
2191
< 0.1%
1971
< 0.1%
1711
< 0.1%
1471
< 0.1%
1431
< 0.1%
1281
< 0.1%
1131
< 0.1%

n
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct829
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.7601764
Minimum0
Maximum8441
Zeros1333
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:18.730355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median49
Q3120
95-th percentile430
Maximum8441
Range8441
Interquartile range (IQR)106

Descriptive statistics

Standard deviation247.7733499
Coefficient of variation (CV)2.159053408
Kurtosis202.8120237
Mean114.7601764
Median Absolute Deviation (MAD)42
Skewness10.29136997
Sum1366219.9
Variance61391.63293
MonotonicityNot monotonic
2022-06-16T15:48:18.848794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01333
 
11.2%
4285
 
2.4%
5215
 
1.8%
7190
 
1.6%
9185
 
1.6%
15168
 
1.4%
14139
 
1.2%
10123
 
1.0%
13121
 
1.0%
11113
 
0.9%
Other values (819)9033
75.9%
ValueCountFrequency (%)
01333
11.2%
144
 
0.4%
1.33
 
< 0.1%
24
 
< 0.1%
310
 
0.1%
4285
 
2.4%
5215
 
1.8%
694
 
0.8%
7190
 
1.6%
882
 
0.7%
ValueCountFrequency (%)
84411
< 0.1%
56691
< 0.1%
53921
< 0.1%
48281
< 0.1%
43081
< 0.1%
40221
< 0.1%
39821
< 0.1%
38961
< 0.1%
38481
< 0.1%
34151
< 0.1%

v
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4275
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678.0428643
Minimum0
Maximum80843.08
Zeros1374
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:19.064337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148.43
median216.64
Q3628.96
95-th percentile2657.328
Maximum80843.08
Range80843.08
Interquartile range (IQR)580.53

Descriptive statistics

Standard deviation1924.506844
Coefficient of variation (CV)2.838326226
Kurtosis418.7158219
Mean678.0428643
Median Absolute Deviation (MAD)201.7
Skewness15.26419787
Sum8072100.3
Variance3703726.594
MonotonicityNot monotonic
2022-06-16T15:48:19.190093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01374
 
11.5%
8283
 
2.4%
11.61215
 
1.8%
19.65168
 
1.4%
27121
 
1.0%
15.5186
 
0.7%
31.765
 
0.5%
51.8962
 
0.5%
48.4357
 
0.5%
34.8756
 
0.5%
Other values (4265)9418
79.1%
ValueCountFrequency (%)
01374
11.5%
13
 
< 0.1%
1.33
 
< 0.1%
24
 
< 0.1%
4.7510
 
0.1%
6.342
 
< 0.1%
8283
 
2.4%
11.091
 
< 0.1%
11.61215
 
1.8%
13.938
 
0.1%
ValueCountFrequency (%)
80843.081
< 0.1%
55140.811
< 0.1%
46943.691
< 0.1%
43342.311
< 0.1%
41217.161
< 0.1%
35928.071
< 0.1%
33814.561
< 0.1%
33034.941
< 0.1%
31233.491
< 0.1%
28092.721
< 0.1%

l
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1418950021
Minimum0
Maximum2
Zeros1405
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:19.323777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.04
median0.08
Q30.17
95-th percentile0.5
Maximum2
Range2
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.1695865487
Coefficient of variation (CV)1.195155194
Kurtosis4.714285184
Mean0.1418950021
Median Absolute Deviation (MAD)0.05
Skewness2.018196398
Sum1689.26
Variance0.02875959751
MonotonicityNot monotonic
2022-06-16T15:48:19.452116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01405
 
11.8%
0.04816
 
6.9%
0.05788
 
6.6%
0.03777
 
6.5%
0.06667
 
5.6%
0.07578
 
4.9%
0.02538
 
4.5%
0.67525
 
4.4%
0.08518
 
4.4%
0.09462
 
3.9%
Other values (50)4831
40.6%
ValueCountFrequency (%)
01405
11.8%
0.01171
 
1.4%
0.02538
 
4.5%
0.03777
6.5%
0.04816
6.9%
0.05788
6.6%
0.06667
5.6%
0.07578
4.9%
0.08518
 
4.4%
0.09462
 
3.9%
ValueCountFrequency (%)
21
 
< 0.1%
1.332
 
< 0.1%
1.33
 
< 0.1%
14
 
< 0.1%
0.921
 
< 0.1%
0.911
 
< 0.1%
0.861
 
< 0.1%
0.67525
4.4%
0.572
 
< 0.1%
0.564
 
< 0.1%

d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2832
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.05158169
Minimum0
Maximum418.2
Zeros1395
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:19.584932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9.08
Q318.75
95-th percentile43.428
Maximum418.2
Range418.2
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation18.33639434
Coefficient of variation (CV)1.304934544
Kurtosis80.9337428
Mean14.05158169
Median Absolute Deviation (MAD)6.92
Skewness5.948563511
Sum167284.08
Variance336.2233576
MonotonicityNot monotonic
2022-06-16T15:48:19.705411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01395
 
11.7%
1.5525
 
4.4%
2423
 
3.6%
2.5275
 
2.3%
3198
 
1.7%
3.5156
 
1.3%
4126
 
1.1%
992
 
0.8%
685
 
0.7%
4.585
 
0.7%
Other values (2822)8545
71.8%
ValueCountFrequency (%)
01395
11.7%
0.51
 
< 0.1%
0.752
 
< 0.1%
14
 
< 0.1%
1.081
 
< 0.1%
1.11
 
< 0.1%
1.171
 
< 0.1%
1.33
 
< 0.1%
1.5525
 
4.4%
1.752
 
< 0.1%
ValueCountFrequency (%)
418.21
< 0.1%
408.731
< 0.1%
384.451
< 0.1%
337.361
< 0.1%
290.151
< 0.1%
269.461
< 0.1%
215.171
< 0.1%
214.51
< 0.1%
213.531
< 0.1%
206.011
< 0.1%

i
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4542
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.76761697
Minimum0
Maximum569.78
Zeros1395
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:19.822478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.72
median22.06
Q337.09
95-th percentile82.214
Maximum569.78
Range569.78
Interquartile range (IQR)25.37

Descriptive statistics

Standard deviation34.48274744
Coefficient of variation (CV)1.158397983
Kurtosis46.55930776
Mean29.76761697
Median Absolute Deviation (MAD)12.21
Skewness4.997091689
Sum354383.48
Variance1189.059871
MonotonicityNot monotonic
2022-06-16T15:48:19.939469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01395
 
11.7%
5.33282
 
2.4%
7.74189
 
1.6%
9.83122
 
1.0%
13.569
 
0.6%
7.7557
 
0.5%
12.6850
 
0.4%
17.7945
 
0.4%
7.8640
 
0.3%
939
 
0.3%
Other values (4532)9617
80.8%
ValueCountFrequency (%)
01395
11.7%
13
 
< 0.1%
1.33
 
< 0.1%
3.11
 
< 0.1%
3.484
 
< 0.1%
41
 
< 0.1%
4.142
 
< 0.1%
4.871
 
< 0.1%
5.33282
 
2.4%
5.431
 
< 0.1%
ValueCountFrequency (%)
569.781
< 0.1%
567.461
< 0.1%
559.621
< 0.1%
524.751
< 0.1%
515.211
< 0.1%
508.421
< 0.1%
473.411
< 0.1%
466.751
< 0.1%
429.871
< 0.1%
415.061
< 0.1%

e
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7588
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35952.62282
Minimum0
Maximum31079782.27
Zeros1395
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:20.061892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1152.36
median2021.97
Q311541.18
95-th percentile109841.42
Maximum31079782.27
Range31079782.27
Interquartile range (IQR)11388.82

Descriptive statistics

Standard deviation416936.1589
Coefficient of variation (CV)11.59682177
Kurtosis2979.813294
Mean35952.62282
Median Absolute Deviation (MAD)2021.97
Skewness47.81012766
Sum428015974.7
Variance1.738357606 × 1011
MonotonicityNot monotonic
2022-06-16T15:48:20.185563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01395
 
11.7%
12282
 
2.4%
17.41189
 
1.6%
39.3120
 
1.0%
5469
 
0.6%
31.0269
 
0.6%
79.2546
 
0.4%
49.1340
 
0.3%
151.3539
 
0.3%
67.535
 
0.3%
Other values (7578)9621
80.8%
ValueCountFrequency (%)
01395
11.7%
14
 
< 0.1%
1.33
 
< 0.1%
4.752
 
< 0.1%
81
 
< 0.1%
12282
 
2.4%
16.641
 
< 0.1%
17.41189
 
1.6%
18.671
 
< 0.1%
23.2223
 
0.2%
ValueCountFrequency (%)
31079782.271
< 0.1%
16846621.121
< 0.1%
12120796.161
< 0.1%
10367010.971
< 0.1%
10100866.91
< 0.1%
9254819.861
< 0.1%
8901671.321
< 0.1%
8255889.431
< 0.1%
6524839.941
< 0.1%
4294926.451
< 0.1%

b
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct320
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2265401092
Minimum0
Maximum26.95
Zeros1897
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:20.311961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.07
Q30.21
95-th percentile0.89
Maximum26.95
Range26.95
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.6418385854
Coefficient of variation (CV)2.833222725
Kurtosis417.8985307
Mean0.2265401092
Median Absolute Deviation (MAD)0.07
Skewness15.24140796
Sum2696.96
Variance0.4119567697
MonotonicityNot monotonic
2022-06-16T15:48:20.439065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01897
 
15.9%
0.01992
 
8.3%
0.02784
 
6.6%
0.03571
 
4.8%
0.04491
 
4.1%
0.06457
 
3.8%
0.05454
 
3.8%
0.07400
 
3.4%
0.08358
 
3.0%
0.09347
 
2.9%
Other values (310)5154
43.3%
ValueCountFrequency (%)
01897
15.9%
0.01992
8.3%
0.02784
6.6%
0.03571
 
4.8%
0.04491
 
4.1%
0.05454
 
3.8%
0.06457
 
3.8%
0.07400
 
3.4%
0.08358
 
3.0%
0.09347
 
2.9%
ValueCountFrequency (%)
26.951
< 0.1%
18.381
< 0.1%
15.651
< 0.1%
14.451
< 0.1%
13.741
< 0.1%
11.981
< 0.1%
11.271
< 0.1%
11.011
< 0.1%
10.411
< 0.1%
9.361
< 0.1%

t
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7345
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.202748
Minimum0
Maximum1726654.57
Zeros1395
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:20.561760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.46
median112.33
Q3641.18
95-th percentile6102.304
Maximum1726654.57
Range1726654.57
Interquartile range (IQR)632.72

Descriptive statistics

Standard deviation23179.53369
Coefficient of variation (CV)11.58859206
Kurtosis2971.446646
Mean2000.202748
Median Absolute Deviation (MAD)112.33
Skewness47.72062725
Sum23812413.71
Variance537290782
MonotonicityNot monotonic
2022-06-16T15:48:20.687375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01395
 
11.7%
0.67282
 
2.4%
0.97189
 
1.6%
2.18120
 
1.0%
1.7269
 
0.6%
369
 
0.6%
4.446
 
0.4%
2.7340
 
0.3%
8.4139
 
0.3%
3.7535
 
0.3%
Other values (7335)9621
80.8%
ValueCountFrequency (%)
01395
11.7%
0.061
 
< 0.1%
0.262
 
< 0.1%
0.441
 
< 0.1%
0.67282
 
2.4%
0.921
 
< 0.1%
0.97189
 
1.6%
13
 
< 0.1%
1.041
 
< 0.1%
1.2925
 
0.2%
ValueCountFrequency (%)
1726654.571
< 0.1%
935923.391
< 0.1%
673377.61
< 0.1%
575945.031
< 0.1%
561159.251
< 0.1%
514156.641
< 0.1%
494537.331
< 0.1%
458660.511
< 0.1%
362491.091
< 0.1%
238607.051
< 0.1%

lOCode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct296
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.37916842
Minimum0
Maximum2824
Zeros1575
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:20.907166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q327
95-th percentile92
Maximum2824
Range2824
Interquartile range (IQR)24

Descriptive statistics

Standard deviation58.7722836
Coefficient of variation (CV)2.31576869
Kurtosis589.6436847
Mean25.37916842
Median Absolute Deviation (MAD)10
Skewness17.46549195
Sum302139
Variance3454.18132
MonotonicityNot monotonic
2022-06-16T15:48:21.020191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01575
 
13.2%
21122
 
9.4%
3446
 
3.7%
4414
 
3.5%
6349
 
2.9%
10341
 
2.9%
9333
 
2.8%
5325
 
2.7%
8315
 
2.6%
7290
 
2.4%
Other values (286)6395
53.7%
ValueCountFrequency (%)
01575
13.2%
1114
 
1.0%
21122
9.4%
3446
 
3.7%
4414
 
3.5%
5325
 
2.7%
6349
 
2.9%
7290
 
2.4%
8315
 
2.6%
9333
 
2.8%
ValueCountFrequency (%)
28241
< 0.1%
15991
< 0.1%
15881
< 0.1%
13391
< 0.1%
12131
< 0.1%
11241
< 0.1%
11071
< 0.1%
9521
< 0.1%
8141
< 0.1%
6561
< 0.1%

lOComment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.10449391
Minimum0
Maximum344
Zeros7771
Zeros (%)65.3%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:21.141383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile16
Maximum344
Range344
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.34981238
Coefficient of variation (CV)3.333816295
Kurtosis255.3986885
Mean3.10449391
Median Absolute Deviation (MAD)0
Skewness11.64669377
Sum36959
Variance107.1186164
MonotonicityNot monotonic
2022-06-16T15:48:21.263729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07771
65.3%
1879
 
7.4%
2499
 
4.2%
3438
 
3.7%
4331
 
2.8%
5263
 
2.2%
6235
 
2.0%
7190
 
1.6%
8144
 
1.2%
9130
 
1.1%
Other values (91)1025
 
8.6%
ValueCountFrequency (%)
07771
65.3%
1879
 
7.4%
2499
 
4.2%
3438
 
3.7%
4331
 
2.8%
5263
 
2.2%
6235
 
2.0%
7190
 
1.6%
8144
 
1.2%
9130
 
1.1%
ValueCountFrequency (%)
3441
< 0.1%
3391
< 0.1%
2061
< 0.1%
1911
< 0.1%
1701
< 0.1%
1651
< 0.1%
1621
< 0.1%
1571
< 0.1%
1411
< 0.1%
1301
< 0.1%

lOBlank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct104
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.901973961
Minimum0
Maximum447
Zeros3568
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:21.393050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile19
Maximum447
Range447
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.63762929
Coefficient of variation (CV)2.170070542
Kurtosis314.3897548
Mean4.901973961
Median Absolute Deviation (MAD)2
Skewness11.73864921
Sum58358
Variance113.1591569
MonotonicityNot monotonic
2022-06-16T15:48:21.510235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03568
30.0%
11535
12.9%
21261
 
10.6%
31149
 
9.7%
4863
 
7.2%
5698
 
5.9%
6393
 
3.3%
7360
 
3.0%
8278
 
2.3%
9249
 
2.1%
Other values (94)1551
13.0%
ValueCountFrequency (%)
03568
30.0%
11535
12.9%
21261
 
10.6%
31149
 
9.7%
4863
 
7.2%
5698
 
5.9%
6393
 
3.3%
7360
 
3.0%
8278
 
2.3%
9249
 
2.1%
ValueCountFrequency (%)
4471
< 0.1%
2191
< 0.1%
2021
< 0.1%
1642
< 0.1%
1561
< 0.1%
1551
< 0.1%
1541
< 0.1%
1431
< 0.1%
1351
< 0.1%
1341
< 0.1%

locCodeAndComment
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3516169677
Minimum0
Maximum108
Zeros10524
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:21.622783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum108
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.838902786
Coefficient of variation (CV)5.229846551
Kurtosis1065.595007
Mean0.3516169677
Median Absolute Deviation (MAD)0
Skewness22.85647256
Sum4186
Variance3.381563455
MonotonicityNot monotonic
2022-06-16T15:48:21.726751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
010524
88.4%
1648
 
5.4%
2254
 
2.1%
3146
 
1.2%
494
 
0.8%
552
 
0.4%
647
 
0.4%
730
 
0.3%
826
 
0.2%
1018
 
0.2%
Other values (20)66
 
0.6%
ValueCountFrequency (%)
010524
88.4%
1648
 
5.4%
2254
 
2.1%
3146
 
1.2%
494
 
0.8%
552
 
0.4%
647
 
0.4%
730
 
0.3%
826
 
0.2%
914
 
0.1%
ValueCountFrequency (%)
1081
< 0.1%
421
< 0.1%
381
< 0.1%
341
< 0.1%
301
< 0.1%
281
< 0.1%
242
< 0.1%
232
< 0.1%
221
< 0.1%
211
< 0.1%

uniq_Op
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct71
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27111298
Minimum0
Maximum411
Zeros1332
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:21.841601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median11
Q316
95-th percentile24
Maximum411
Range411
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.951867006
Coefficient of variation (CV)0.8829533539
Kurtosis490.8688596
Mean11.27111298
Median Absolute Deviation (MAD)5
Skewness13.78338316
Sum134182.6
Variance99.0396569
MonotonicityNot monotonic
2022-06-16T15:48:21.958227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01332
 
11.2%
11701
 
5.9%
12640
 
5.4%
9597
 
5.0%
4582
 
4.9%
10575
 
4.8%
3565
 
4.7%
13555
 
4.7%
14554
 
4.7%
5538
 
4.5%
Other values (61)5266
44.2%
ValueCountFrequency (%)
01332
11.2%
149
 
0.4%
1.23
 
< 0.1%
27
 
0.1%
3565
4.7%
4582
4.9%
5538
4.5%
6531
 
4.5%
7526
 
4.4%
8508
 
4.3%
ValueCountFrequency (%)
4111
< 0.1%
4101
< 0.1%
2801
< 0.1%
1721
< 0.1%
1551
< 0.1%
1051
< 0.1%
1021
< 0.1%
991
< 0.1%
981
< 0.1%
781
< 0.1%

uniq_Opnd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct178
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.05263335
Minimum0
Maximum1026
Zeros1394
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:22.079242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q321
95-th percentile54
Maximum1026
Range1026
Interquartile range (IQR)17

Descriptive statistics

Standard deviation26.942903
Coefficient of variation (CV)1.579984888
Kurtosis337.5998022
Mean17.05263335
Median Absolute Deviation (MAD)8
Skewness12.59332294
Sum203011.6
Variance725.9200218
MonotonicityNot monotonic
2022-06-16T15:48:22.202470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01394
 
11.7%
6578
 
4.9%
5477
 
4.0%
7470
 
3.9%
4458
 
3.8%
8455
 
3.8%
2430
 
3.6%
3416
 
3.5%
10415
 
3.5%
11405
 
3.4%
Other values (168)6407
53.8%
ValueCountFrequency (%)
01394
11.7%
1358
 
3.0%
1.23
 
< 0.1%
2430
 
3.6%
3416
 
3.5%
4458
 
3.8%
5477
 
4.0%
6578
4.9%
7470
 
3.9%
8455
 
3.8%
ValueCountFrequency (%)
10261
< 0.1%
8111
< 0.1%
8061
< 0.1%
6091
< 0.1%
5491
< 0.1%
4071
< 0.1%
3551
< 0.1%
3251
< 0.1%
3141
< 0.1%
2791
< 0.1%

total_Op
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct603
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.56653507
Minimum0
Maximum5420
Zeros1332
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:22.320710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median29
Q372
95-th percentile256
Maximum5420
Range5420
Interquartile range (IQR)64

Descriptive statistics

Standard deviation150.5571409
Coefficient of variation (CV)2.19578167
Kurtosis234.2130466
Mean68.56653507
Median Absolute Deviation (MAD)25
Skewness11.03099676
Sum816284.6
Variance22667.45268
MonotonicityNot monotonic
2022-06-16T15:48:22.445757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01332
 
11.2%
3483
 
4.1%
5250
 
2.1%
8244
 
2.0%
6232
 
1.9%
9209
 
1.8%
4208
 
1.7%
7203
 
1.7%
10191
 
1.6%
11182
 
1.5%
Other values (593)8371
70.3%
ValueCountFrequency (%)
01332
11.2%
149
 
0.4%
1.23
 
< 0.1%
25
 
< 0.1%
3483
 
4.1%
4208
 
1.7%
5250
 
2.1%
6232
 
1.9%
7203
 
1.7%
8244
 
2.0%
ValueCountFrequency (%)
54201
< 0.1%
33681
< 0.1%
31721
< 0.1%
31491
< 0.1%
27521
< 0.1%
25951
< 0.1%
24691
< 0.1%
24161
< 0.1%
24151
< 0.1%
21541
< 0.1%

total_Opnd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct479
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.38703066
Minimum0
Maximum3021
Zeros1394
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:22.659627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19
Q348
95-th percentile176
Maximum3021
Range3021
Interquartile range (IQR)43

Descriptive statistics

Standard deviation99.42651495
Coefficient of variation (CV)2.143411931
Kurtosis161.2420722
Mean46.38703066
Median Absolute Deviation (MAD)17
Skewness9.367670085
Sum552237.6
Variance9885.631875
MonotonicityNot monotonic
2022-06-16T15:48:22.776259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01394
 
11.7%
2366
 
3.1%
1350
 
2.9%
4338
 
2.8%
7312
 
2.6%
3297
 
2.5%
6283
 
2.4%
5254
 
2.1%
8247
 
2.1%
10243
 
2.0%
Other values (469)7821
65.7%
ValueCountFrequency (%)
01394
11.7%
1350
 
2.9%
1.23
 
< 0.1%
2366
 
3.1%
3297
 
2.5%
4338
 
2.8%
5254
 
2.1%
6283
 
2.4%
7312
 
2.6%
8247
 
2.1%
ValueCountFrequency (%)
30211
< 0.1%
23011
< 0.1%
22431
< 0.1%
17301
< 0.1%
16071
< 0.1%
15561
< 0.1%
15221
< 0.1%
15131
< 0.1%
14831
< 0.1%
14321
< 0.1%

branchCount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct152
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.14163797
Minimum1
Maximum826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.1 KiB
2022-06-16T15:48:22.903683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q311
95-th percentile37
Maximum826
Range825
Interquartile range (IQR)10

Descriptive statistics

Standard deviation22.39972925
Coefficient of variation (CV)2.010452082
Kurtosis251.9319613
Mean11.14163797
Median Absolute Deviation (MAD)4
Skewness11.43010649
Sum132641.2
Variance501.7478705
MonotonicityNot monotonic
2022-06-16T15:48:23.020256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13011
25.3%
31903
16.0%
51439
12.1%
71036
 
8.7%
9796
 
6.7%
11611
 
5.1%
13425
 
3.6%
15324
 
2.7%
17253
 
2.1%
19238
 
2.0%
Other values (142)1869
15.7%
ValueCountFrequency (%)
13011
25.3%
1.43
 
< 0.1%
31903
16.0%
441
 
0.3%
51439
12.1%
648
 
0.4%
71036
 
8.7%
828
 
0.2%
9796
 
6.7%
1023
 
0.2%
ValueCountFrequency (%)
8261
< 0.1%
5031
< 0.1%
4851
< 0.1%
4641
< 0.1%
4051
< 0.1%
4031
< 0.1%
4011
< 0.1%
3611
< 0.1%
3441
< 0.1%
3381
< 0.1%

defects
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.1 KiB
0
9643 
1
2262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09643
81.0%
12262
 
19.0%

Length

2022-06-16T15:48:23.126189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-16T15:48:23.184462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
09643
81.0%
12262
 
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-06-16T15:48:14.229760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:21.964093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:24.496467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:27.079297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:29.405509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:31.913357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:34.514403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:36.933029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:39.475233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:42.100260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:44.516800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:46.997201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:49.431724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:51.853246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:54.444324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:56.911332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:59.396102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:01.833184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:04.323528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:06.788907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:09.209182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:11.697530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:14.333483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:22.086240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:24.606694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:27.184864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:29.512577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:32.026086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:34.617005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:37.046729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:39.589898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:42.205009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:44.623514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:47.105938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:49.537437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:51.965944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:54.549011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:57.022007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:59.499851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:01.941865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:04.426251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:06.893627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:09.314873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:11.805241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:14.444186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:22.201159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:24.724202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:27.293962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:29.627886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:32.143764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:34.726738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:37.167402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:39.712571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:42.312722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:54.664729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:57.140720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:59.610556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:02.055562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:04.536930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:07.007324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:09.427572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:11.919935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:14.541898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:22.307848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:27.395429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:29.730073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:32.251453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:34.824469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:37.277105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:39.909066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:42.411458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:44.850907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:47.325324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:49.746877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:52.188350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:54.766457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:57.247406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:59.711259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:02.159314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:04.644639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:07.118027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:09.530323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:12.023658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:14.649610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:22.420598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:24.946868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:27.500733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:29.928760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:32.366146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:34.930166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:37.393771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:40.024763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:42.516149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:44.961631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:47.438051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:49.942327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:52.305038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:54.876164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:57.362100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:59.904761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:02.269988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:04.761349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:07.226751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:09.640002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:12.138324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:14.762335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:22.536927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:25.070112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:27.614922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:30.046160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:32.486824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:35.042866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:37.515445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:40.145442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:42.626853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:45.076304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:47.554738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:50.056024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:52.425689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:54.991857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:57.486790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:00.017440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:02.386676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:04.882033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:07.343440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:09.755723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:26.214037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:28.684898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:31.163362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:33.731498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:36.131146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:38.682355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:41.308377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:43.768800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:46.165420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:48.675741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:51.128157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:53.662382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:56.083268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:58.624070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:01.115103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:03.571537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:05.977077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:08.468543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:48:13.470761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:15.957114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:48:13.576478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:31.374797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:36.331638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:38.908720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:41.533802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:43.982229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:46.374833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:51.335631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:48:01.317534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:48:11.166921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:13.688202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:16.164587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:31.479517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:34.062611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:39.019423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:48.995885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:53.994494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:56.393718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:29.090817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:48:11.376361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:48:16.372032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:47:24.281262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:47:36.831301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:48:01.734448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:04.219776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-16T15:48:09.105459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:11.592809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-16T15:48:14.125040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-06-16T15:48:23.276664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-16T15:48:23.512328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-16T15:48:23.748392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-16T15:48:23.987804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-16T15:48:16.791902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-16T15:48:17.204076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexlocv(g)ev(g)iv(g)nvldiebtlOCodelOCommentlOBlanklocCodeAndCommentuniq_Opuniq_Opndtotal_Optotal_OpndbranchCountdefects
001.11.41.41.41.31.301.301.301.301.301.301.3022221.21.21.21.21.40
111.01.01.01.01.01.001.001.001.001.001.001.0011111.01.01.01.01.01
2224.05.01.03.063.0309.130.119.5032.542936.770.10163.15106015.015.044.019.09.00
3320.04.04.02.047.0215.490.0616.0013.473447.890.07191.55003016.08.031.016.07.00
4424.06.06.02.072.0346.130.0617.3319.975999.580.12333.31003016.012.046.026.011.00
5524.06.06.02.072.0346.130.0617.3319.975999.580.12333.31003016.012.046.026.011.00
667.01.01.01.011.034.870.502.0017.4369.740.013.8700104.05.06.05.01.00
7712.02.01.02.023.094.010.166.4314.62604.360.0333.58007010.07.014.09.03.00
8825.05.05.05.0107.0548.830.0714.2538.517820.870.18434.49121613015.020.069.038.09.00
9946.015.03.01.0239.01362.410.0422.3061.1030377.950.451687.6683522015.037.0129.0110.029.00

Last rows

df_indexlocv(g)ev(g)iv(g)nvldiebtlOCodelOCommentlOBlanklocCodeAndCommentuniq_Opuniq_Opndtotal_Optotal_OpndbranchCountdefects
1189551210.02.01.02.018.064.530.147.009.22451.710.0225.0980008.04.011.07.03.01
118965138.01.01.01.010.030.000.303.339.00100.000.015.5640105.03.06.04.01.01
118975148.01.01.01.010.030.000.303.339.00100.000.015.5640105.03.06.04.01.01
1189851514.03.01.01.032.0128.000.224.5028.44576.000.0432.0090306.010.017.015.05.01
118995166.02.01.01.015.053.770.402.5021.51134.440.027.4720205.07.08.07.03.01
119005174.01.01.01.05.011.610.502.005.8023.220.001.2920004.01.04.01.01.01
119015184.01.01.01.04.08.000.671.505.3312.000.000.6720003.01.03.01.01.01
119025194.01.01.01.04.08.000.671.505.3312.000.000.6720003.01.03.01.01.01
119035204.01.01.01.05.011.610.671.507.7417.410.000.9720003.02.03.02.01.01
119045213.01.01.01.01.00.000.000.000.000.000.000.0010001.00.01.00.01.01

Duplicate rows

Most frequently occurring

df_indexlocv(g)ev(g)iv(g)nvldiebtlOCodelOCommentlOBlanklocCodeAndCommentuniq_Opuniq_Opndtotal_Optotal_OpndbranchCountdefects# duplicates
001.11.41.41.41.31.31.31.31.31.31.31.322221.21.21.21.21.403
111.01.01.01.01.01.01.01.01.01.01.01.011111.01.01.01.01.013